How to reliably connect LLMs to real-world data and systems (www.techradar.com)

Anthropic has introduced the model context protocol (MCP), an open-source standard aimed at enhancing how large language models (LLMs) like Claude and Cursor interact with external tools, databases, and data sources. This protocol allows for a more dynamic integration, enabling models to seamlessly orchestrate tasks such as querying databases and managing workflows without the need for extensive hard-coded setups. This flexibility is poised to reduce engineering overhead and foster more agent-like behavior in AI applications, moving beyond simple prompt-response interactions. However, the adoption of MCP comes with challenges, particularly the risk of "tool overload." As models gain access to a multitude of tools, their ability to make appropriate selections diminishes, increasing the likelihood of errors or unintended actions—what some term "action hallucinations." To mitigate these issues, the integration of graph-based approaches, referred to as GraphRAG, is gaining traction. By incorporating knowledge graphs alongside MCP, developers can provide LLMs with structured context about data relationships, improving tool selection and execution. This combination aims to enhance AI reliability, making systems not only more capable but also better equipped to navigate potential pitfalls, thus paving the way for more practical AI applications.